As full-duplex hands-free communication has become ever more widespread, attention has been focussed on the problem of acoustic echoes that result from coupling between loudspeakers and microphones. Acoustic echo cancellers are now commonly used in such systems to reduce this undesired effect. An acoustic echo canceller (AEC) derives an estimate of the echo using a finite impulse response (FIR) filter with adjustable or adaptive coefficients to model the response of the acoustic coupling path. The estimated echo signal may then be subtracted from the undesired echo signal, which substantially cancels the echo.
A similar technique is also used in line echo cancellation. In line echo cancellation applications, however, the unwanted echo signal is created by communication circuit anomalies, such as cross-talk.
In both of these echo cancellation applications, the adaptive coefficients of the FIR filter are derived from the input signal and the echo signal using a version of the known least-mean-square (LMS) algorithm, owing to its simplicity of implementation and robust operation. The LMS algorithm uses iterative techniques to minimize the square of the error signal, which is the difference between the actual echo signal and the estimated echo signal produced by the FIR filter.
The use of adaptive filters, as opposed to fixed filters, allows echo cancellers to respond to changes in the echo creating environment. Consider an FIR filter used in an AEC application. Without the use of adaptive coefficients, the impulse response of the acoustic echo path must be estimated and then appropriate filter coefficients must be permanently programmed. Because of variations in room acoustics due to changes in furniture configuration, occupancy, or microphone or speaker placement, a set of permanently programmed coefficients for the FIR filter will not necessarily provide the best echo cancellation.
Adaptive filters, however, also have problems that limit their performance. A particular problem associated with known adaptive FIR filters is the slow convergence of the LMS algorithm. Convergence speed is measured by the amount of time, or number of samples, it takes for the LMS algorithm to generate a set of coefficients that best represents the echo path response. Until the LMS algorithm converges, the error signal will exceed its minimum value. Slow convergence, therefore, detrimentally prolongs the duration of the non-minimum error signal. The slow convergence problem has been noted in several publications and patents including U.S. Pat. Nos. 5,014,263 and 5,263,019, which are incorporated by reference herein.
Prior solutions to the problem of slow convergence include the addition of a whitening filter to broaden the spectrum of the adaptive filter's input signal. After broadening the spectrum, the adaptive FIR filter then performs the echo cancellation. An inverse whitening filter is then provided to remove the broadening effects. This solution, however, while initially increasing the rate of convergence, does not appreciably alleviate the slow convergence as the error approaches the minimum.
The slow convergence that is experienced as the error signal approaches its minimum is referred to as slow asymptotic convergence. One reason prior art solutions have not successfully reduced the error due to slow asymptotic convergence of adaptive filters using the LMS algorithm is that there has been insufficient knowledge as to its cause and character.